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A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation.

Jinghua Zhang¹, Chen Li¹, Frank Kulwa¹

¹Microscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang 110169, China.

Biomed Research International

|July 30, 2020

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A new Multiscale CNN-CRF (MSCC) framework effectively segments Environmental Microorganisms (EMs). This method enhances segmentation accuracy and reduces memory usage for researchers.

Area of Science:

Microbiology
Computer Vision
Image Analysis

Background:

Accurate identification of Environmental Microorganisms (EMs) is crucial for research.
Existing image segmentation methods face challenges in detail and efficiency.

Purpose of the Study:

To propose an effective framework for Environmental Microorganism (EM) image segmentation.
To improve segmentation accuracy, detail preservation, and computational efficiency.

Main Methods:

Developed a Multiscale CNN-CRF (MSCC) framework.
Implemented a novel pixel-level segmentation using mU-Net-B3 Convolutional Neural Network (CNN) with Conditional Random Field (CRF) postprocessing.
Utilized a VGG-16 based patch-level segmentation with a buffer strategy for enhanced detail.

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Main Results:

The MSCC method achieved improved evaluation indexes: Dice (87.13%), Jaccard (79.74%), Recall (87.12%), and Accuracy (96.91%).
Significantly reduced memory requirements from 355 MB to 103 MB.
Decreased volume overlap error from 22.58% to 20.26% compared to state-of-the-art methods.

Conclusions:

The proposed MSCC framework demonstrates superior performance in EM image segmentation.
MSCC offers a promising solution for efficient and accurate analysis of Environmental Microorganisms.
The method shows great potential for advancing research in the EM segmentation field.